Assignment_4

Author

Daniel Nikolai Johannessen

Task 1

1: For the last 3 months of 2017, calculate the total Sales by month, for Region 1 and Region 9 in the Customer_Segment, Corporate, and Consumer. This output is Table 1.

Region Month Customer segment Total sales
Region 1 10 Consumer 815.790
Region 1 10 Corporate 295.060
Region 1 11 Consumer 9480.470
Region 1 11 Corporate 8564.840
Region 1 12 Consumer 210.060
Region 1 12 Corporate 13260.838
Region 9 10 Consumer 5908.890
Region 9 10 Corporate 16781.431
Region 9 11 Consumer 192.330
Region 9 11 Corporate 5463.130
Region 9 12 Corporate 9377.647

2: Make a plot of the monthly total Sales in Region 1 and Region 13 in 2015, 2016, and 2017. This output is Figure 1.

Figure for monthly sales in Region 1 and 13 in 2015-2017

3: In Figure 1, identify the months where the total Sales in Region 13 is greater than the total Sales in Region 1. This output is Table 2.

Month Year Region 1 Region 13
jun 2015 12844.97 32306.88
aug 2015 2266.96 13985.22
okt 2015 11058.13 14885.24
nov 2015 13290.26 49685.99
des 2015 11048.17 19514.99
jan 2016 2362.43 10407.92
feb 2016 33085.29 55631.95
mai 2016 22068.67 22821.70
des 2016 17020.27 26889.71
jun 2017 10335.31 17430.46

4: Find the average Profit per Customer_Segment and Product_Category in 2017, for all regions except Region 3, 5 and 8. What segment produced the highest average profit? This output is Table 3.

Customer segment Product category Average profit
Small Business Technology 544.442933
Corporate Technology 413.915696
Home Office Technology 271.119368
Consumer Technology 223.123188
Corporate Office Supplies 164.827620
Consumer Office Supplies 107.459323
Small Business Office Supplies 93.995230
Home Office Office Supplies 71.364500
Consumer Furniture 19.551912
Small Business Furniture 16.874722
Home Office Furniture 7.511408
Corporate Furniture -88.222121

Task 2

In this task, feel free to use any API or package/library that downloads the data to your session. Use code and download daily stock prices for Exxon Mobil Corporation (XOM), traded at NYSE. The Yahoo! Finance site is a convenient place to find the data . Use the Adjusted closing price from January 4th 2010 as the starting date. And calculate the monthly average using trading volume as a weight, and save this variable as  “exxon”.

Use code to download the daily Brent Crude Oil Price from FRED  from January 4th 2010 as the starting date. And calculate the monthly arithmetic average. Save the monthly arithmetic average values as  “oil”. 

In both variables, take December 2022, or 2022:12 for shorthand as a last data point.

Plot both variables, i.e., exxon and oil ” in the same plot window. Here, the x-axis should be the  “date” variable.  Comment on the plots. 

Figure for Exxon share price and Oil price

Exxon’s stock price appears to correlate more closely to the oil price after 2015. We can see that they werent very effected by the drop in oil prices from 2014 to 2015 but after this year, they appear to be prices around the same as a barrel of oil.

Now take “exxon” as a y-variable and “oil” as an x-variable. 

Figure for exxon as y and oil as x

Use R’s  lm() function. Set the variable on the y-axis and x-axis, and specify the data set. 


Call:
lm(formula = exxon ~ oil, data = task2)

Coefficients:
(Intercept)          oil  
    47.7232       0.1271  

After “running” the code, how do you interpret the estimated coefficients? 

The intercept coefficient shows the estimated value of exxon when oil is equal to 0, but it may now have a practical interpretation in this context since its unlikely for oil price to be 0.

The slope coefficient of 0.1282 means that for every one increase in x, exxon’s value will increase by 0.1282. This means there is a positive linear relationship between oil prices and Exxon’s share price, indicating that higher oil prices are associated with higher share prices for Exxon.

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